The BMF problem is NP-hard [1]}, leading to the development of a variety of strategies to approximate a solution. Greedy algorithms were some of the first proposed methods to solving BMF [1]}, [3]}, [4]}. There is also a family of Bayesian methods. For example, the authors in [5]} use a probabilistic generative model and derive a sampling method to accelerate the optimization for BMF. In [6]} they use a Bayesian framework to consider the BMF problem as a maximum log likelihood problem and use a message passing procedure to approximate the MAP assignment.
